注意力引导多任务学习的前列腺癌盆腔淋巴结转移预测

张志远, 胡冀苏, 张跃跃, 钱旭升, 周志勇, 戴亚康

Attention-Guided Multi-Task Learning for Prostate Cancer Pelvic Lymph Node Metastasis Prediction

ZHANG Zhiyuan, HU Jisu, ZHANG Yueyue, QIAN Xusheng, ZHOU Zhiyong, DAI Yakang

表3 所提方法与其他经典单任务分类方法对比

Tab.3 Comparison of proposed method and other existing state-of-the-art single-task methods

方法 (平均值±方差)/% AUPRC AUROC SEN SPE ACC F1 CBAMResNet50[23] 78.83±5.01 89.33±3.68 58.25±12.11 90.79±5.12 81.55±4.44 63.88±9.64 DenseNet121[24] 82.03±3.38 90.24±1.70 67.08±11.58 90.35±6.25 83.73±2.76 69.87±5.14 EfficientNet[25] 80.10±6.68 88.25±3.84 64.74±5.55 87.36±11.00 80.93±8.67 66.82±10.87 InceptionV4[26] 83.15±2.13 90.31±1.21 78.01±9.64 81.71±10.11 80.67±6.28 70.02±6.34 SeResNet50[27] 83.30±4.80 90.30±3.18 73.63±2.41 92.14±4.50 86.88±3.58 76.34±5.13 本文方法 85.44±2.04 91.86±2.18 73.74±12.20 94.33±3.29 88.45±2.30 78.10±5.38